Computational Intelligence Applied to the Automatic Monitoring of Dressing Operations in an Industrial CNC Machine

Braga, Arthur P. S.; Carvalho, André C. P. L. F. de; Oliveira, João Fernando Gomes de

doi:10.1007/978-3-540-78297-1_12

Cited by 1 publication

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References 14 publications

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“…Many researchers have described the application of ML methodologies across different scientific and practical domains. Numerous research papers discuss Kohonen networks applied for multidimensional data visualization to evaluate classification possibilities of various coal types [19], collision free path planning and control of wheeled mobile robot [20], parametric fault clustering in analog electronic circuits with the use of a self-organizing artificial neural network [21], intrusion detection in software defined networks [22], simulating the milling cutter trajectory [23], or automated monitoring of the surface grinding process [24], with the use of multilayer perception (MLP) network, radial basis function network (RBFN), support vector machine (SVM), and the decision tree.…”

Section: Introductionmentioning

confidence: 99%

Bulletin of the Polish Academy of Sciences: Technical Sciences

Rojek¹,

Dostatni²

2020

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Machine learning (ML) methods facilitate automated data mining. The authors compare the effectiveness of selected ML methods (RBF networks, Kohonen networks, and random forest) as modelling tools supporting the selection of materials in ecodesign. Applied in the design process, ML methods help benefit from the knowledge, experience and creativity of designers stored in historical data in databases. Implemented into a decision support system, the knowledge can be utilized -in the case under analysis -in the process of design of environmentally friendly products. The study was initiated with an analysis of input data for the selection of materials. The input data, specified in cooperation with designers, include both technological and environmental parameters which guarantee the desired compatibility of materials. Next, models were developed using selected ML methods. The models were assessed and implemented into an expert system. The authors show which models best fit their purpose and why. Models supporting the selection of materials, connections and disassembly methods help boost the recycling properties of designed products.

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Section: Introductionmentioning

confidence: 99%

Bulletin of the Polish Academy of Sciences: Technical Sciences

Rojek¹,

Dostatni²

2020

View full text Add to dashboard Cite

show abstract

Computational Intelligence Applied to the Automatic Monitoring of Dressing Operations in an Industrial CNC Machine

Cited by 1 publication

References 14 publications

Bulletin of the Polish Academy of Sciences: Technical Sciences

Bulletin of the Polish Academy of Sciences: Technical Sciences

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